This code covers chapter 6 of “Introduction to Data Mining” by Pang-Ning Tan, Michael Steinbach and Vipin Kumar.

This work is licensed under the Creative Commons Attribution 4.0 International License. For questions please contact Michael Hahsler.

library(arules)

## Loading required package: Matrix

## 
## Attaching package: 'arules'

## The following objects are masked from 'package:base':
## 
##     abbreviate, write

library(arulesViz)

## Loading required package: grid

Load the data set

data(Groceries)
summary(Groceries)

## transactions as itemMatrix in sparse format with
##  9835 rows (elements/itemsets/transactions) and
##  169 columns (items) and a density of 0.02609146 
## 
## most frequent items:
##       whole milk other vegetables       rolls/buns             soda 
##             2513             1903             1809             1715 
##           yogurt          (Other) 
##             1372            34055 
## 
## element (itemset/transaction) length distribution:
## sizes
##    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
## 2159 1643 1299 1005  855  645  545  438  350  246  182  117   78   77   55 
##   16   17   18   19   20   21   22   23   24   26   27   28   29   32 
##   46   29   14   14    9   11    4    6    1    1    1    1    3    1 
## 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   2.000   3.000   4.409   6.000  32.000 
## 
## includes extended item information - examples:
##        labels  level2           level1
## 1 frankfurter sausage meat and sausage
## 2     sausage sausage meat and sausage
## 3  liver loaf sausage meat and sausage

inspect(head(Groceries))

##     items                     
## [1] {citrus fruit,            
##      semi-finished bread,     
##      margarine,               
##      ready soups}             
## [2] {tropical fruit,          
##      yogurt,                  
##      coffee}                  
## [3] {whole milk}              
## [4] {pip fruit,               
##      yogurt,                  
##      cream cheese ,           
##      meat spreads}            
## [5] {other vegetables,        
##      whole milk,              
##      condensed milk,          
##      long life bakery product}
## [6] {whole milk,              
##      butter,                  
##      yogurt,                  
##      rice,                    
##      abrasive cleaner}

Mine Association Rules

rules <- apriori(Groceries, parameter=list(support=0.001, confidence=.8))

## Apriori
## 
## Parameter specification:
##  confidence minval smax arem  aval originalSupport maxtime support minlen
##         0.8    0.1    1 none FALSE            TRUE       5   0.001      1
##  maxlen target   ext
##      10  rules FALSE
## 
## Algorithmic control:
##  filter tree heap memopt load sort verbose
##     0.1 TRUE TRUE  FALSE TRUE    2    TRUE
## 
## Absolute minimum support count: 9 
## 
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.01s].
## sorting and recoding items ... [157 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 done [0.02s].
## writing ... [410 rule(s)] done [0.00s].
## creating S4 object  ... done [0.00s].

inspectDT(rules)

plot(rules, engine = "html")

## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.

plot(rules, method = "graph", engine = "html")

## Warning: Too many rules supplied. Only plotting the best 100 rules using
## lift (change control parameter max if needed)